Auto Accidents(1975-2017)

Import cuXfilter

[1]:

import cuXfilter
from cuXfilter import charts
from cuXfilter.layouts import feature_and_base

[2]:

#update data_dir if you have downloaded datasets elsewhere
DATA_DIR = './data'

Download required datasets

[3]:

from cuXfilter.sampledata import datasets_check
datasets_check('auto_accidents', base_dir=DATA_DIR)

Dataset - ./data/auto_accidents.arrow

dataset already downloaded

[4]:

cux_df = cuXfilter.DataFrame.from_arrow('./data/auto_accidents.arrow')
cux_df.data['ST_CASE'] = cux_df.data['ST_CASE'].astype('float64')

Define Charts

[5]:

# add mappings for day of week
label_map = {
    1: 'Sunday',
    2: 'Monday',
    3: 'Tuesday',
    4: 'Wednesday',
    5: 'Thursday',
    6: 'Friday',
    7: 'Saturday',
    9: 'Unknown'
}

gtc_demo_red_blue_palette = [ (49,130,189), (107,174,214), (123, 142, 216), (226,103,152), (255,0,104) , (50,50,50) ]

[6]:

from bokeh.tile_providers import get_provider as gp
tile_provider = gp('CARTODBPOSITRON')




Uncomment the below lines and replace MAPBOX\_TOKEN with mapbox token string if you want to use mapbox map-tiles. Can be created for free here -https://www.mapbox.com/help/define-access-token/

[7]:

#from cuXfilter.assets.custom_tiles import get_provider, Vendors
#tile_provider = get_provider(Vendors.MAPBOX_LIGHT, access_token=MAPBOX_TOKEN)

[13]:

chart1 = charts.cudatashader.scatter_geo(x='dropoff_x', y='dropoff_y', aggregate_col='ST_CASE',
                                        tile_provider=tile_provider,
                                         color_palette=gtc_demo_red_blue_palette,)

chart2 = charts.bokeh.bar('YEAR')

chart3 = charts.panel_widgets.multi_select('DAY_WEEK', label_map=label_map)

Create a dashboard object

[14]:

d = cux_df.dashboard([chart1, chart3, chart2], layout=cuXfilter.layouts.feature_and_base,theme = cuXfilter.themes.rapids, title='Auto Accident Dataset')

[15]:

#dashboard object
d

[15]:

Starting the dashboard

d.show(‘url you want the dashboard to run’) remote dashboard d.app(‘10.110.47.43:8888’) within the notebook: If you are using jupyter remotely, use this line instead of second, and replace url with current notebook url

[16]:

await d.preview()
../_images/examples_auto_accidents_example_18_2.png

Export the queried data into a dataframe

[12]:

queried_df = d.export()

no querying done, returning original dataframe